Developing Clinically Interpretable Neuroimaging Biotypes in Psychiatry

Despite available treatments, major depressive disorder (MDD) remains one of the leading causes of disability across medical conditions. The current symptom-based diagnostic system groups patients with highly heterogeneous presentations, with no biomarkers to guide treatment, akin to diagnosing heart disease solely by chest pain without imaging to reveal the underlying pathology. Lacking biological guidance, clinicians rely on trial-and-error prescribing. Only 33% of individuals with MDD achieve remission in response to initial treatments, and most cycle through multiple treatments over an average of 7 years. The risk of relapse increases with each treatment failure, rising from 50% to 90%. This critical review synthesizes studies showing how functional magnetic resonance imaging (fMRI) can predict treatment outcomes and identify which treatment is most effective for an individual based on their brain circuit profile. We illustrate one such method, a theoretically informed approach that quantifies dysfunction across 6 large-scale biotype circuits, relative to healthy reference norms. The resulting personalized circuit scores serve as predictors of response or failure and as moderators of differential treatment outcomes. Matching treatment to a patient's biotype, defined by their personalized circuit scores, has the potential to double remission rates compared with unmatched treatment. We place this example in the broader context of precision imaging approaches to parsing MDD heterogeneity. We also discuss key challenges, limitations, and future directions for translating fMRI-based tools into clinical practice.